ML Kit for Firebaseplat_iosplat_android

Use machine learning in your apps to solve real-world problems.

ML Kit is a mobile SDK that brings Google's machine learning expertise to
Android and iOS apps in a powerful yet easy-to-use package. Whether you're new
or experienced in machine learning, you can implement the functionality
you need in just a few lines of code. There's no need to have deep knowledge of
neural networks or model optimization to get started. On the other hand, if you
are an experienced ML developer, ML Kit provides convenient APIs that help
you use your custom TensorFlow Lite models in your mobile apps.

This is a beta release of ML Kit for Firebase. This
API might be changed in backward-incompatible ways and is not subject to any
SLA or deprecation policy.

Key capabilities

Production-ready for common use cases

ML Kit comes with a set of ready-to-use APIs for common mobile use
cases: recognizing text, detecting faces, identifying landmarks, scanning
barcodes, labeling images, and identifying the language of text. Simply
pass in data to the ML Kit library and it gives you the information you
need.

On-device or in the cloud

ML Kit’s selection of APIs run on-device or in the cloud. Our
on-device APIs can process your data quickly and work even when
there’s no network connection. Our cloud-based APIs, on the other hand,
leverage the power of Google Cloud Platform's machine learning technology
to give you an even higher level of accuracy.

Deploy custom models

If ML Kit's APIs don't cover your use cases, you can always bring your
own existing TensorFlow Lite models. Just upload your model to
Firebase, and we'll take care of hosting and serving it to your app.
ML Kit acts as an API layer to your custom model, making it simpler to
run and use.

How does it work?

ML Kit makes it easy to apply ML techniques in your apps by bringing Google's
ML technologies, such as the
Google Cloud Vision API,
TensorFlow Lite, and the
Android Neural Networks API
together in a single SDK. Whether you need the power of cloud-based processing,
the real-time capabilities of mobile-optimized on-device models, or the
flexibility of custom TensorFlow Lite models, ML Kit makes it possible with
just a few lines of code.

Implementation path

For example, if you're using a vision feature, capture an image from the
camera and generate the necessary metadata such as image rotation, or prompt
the user to select a photo from their gallery.

Apply the ML model to your data

By applying the ML model to your data, you generate insights such as
the emotional state of detected faces or the objects and concepts that were
recognized in the image, depending on the feature you used. Use these
insights to power features in your app like photo embellishment, automatic
metadata generation, or whatever else you can imagine.